Apparatus and method for controlling autonomous vehicle

ABSTRACT

An apparatus and method for controlling an autonomous vehicle are provided. The apparatus includes a user input unit that receives identification information of a driver within the vehicle during autonomous driving and an information collection unit that acquires a global route of the vehicle and surrounding environment information. A controller determines a learning section on the global route based on the surrounding environment information and outputs a driving pattern of the driver by performing repetitive learning based on operation information of the driver in the learning section. Autonomous driving of the vehicle is then executed based on the output driving pattern of the driver.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the benefit of Korean Patent Application No.10-2019-0060651, filed on May 23, 2019, which is hereby incorporated byreference as if fully set forth herein.

BACKGROUND Field of the Invention

The present invention relates to an apparatus and method for controllingan autonomous vehicle, and more particularly, to an apparatus and methodfor controlling an autonomous vehicle in which user-friendly autonomousdriving control is performed by reflecting a driving pattern learned bya driver operation.

Discussion of the Related Art

Recently, interest in autonomous vehicles is rapidly increasing. Inautonomous vehicles, advanced driver assistance systems (ADASs) areapplied, and may thus liberate drivers from monotonous work, such asoperation of a steering wheel and a pedal operation during driving ofthe vehicle, and reduce driver's erroneous operation thus preventingaccidents.

The autonomous vehicles which are commercialized now prevent collisionwith an object by calculating a time to collision (TTC) based oninformation detected by sensors mounted in the vehicle, performautonomous driving based on a monolithic driving strategy and adjustparameters and thus have limits in reflection of driving tendencies ofvarious drivers, and further cause the driver to feel sense ofdifference between real driving and autonomous driving and thus lowerride comfort. Further, since it is difficult to consider all variableswhich may be generated in a real road environment which isunpredictable, the autonomous vehicles perform passive correspondencefocused on safety rather than flexible correspondence.

SUMMARY

Accordingly, the present invention provides an apparatus and method forcontrolling an autonomous vehicle that substantially obviate one or moreproblems due to limitations and disadvantages of the related art. Anobject of the present invention is to provide an apparatus and methodfor controlling an autonomous vehicle in which user-friendly autonomousdriving control may be performed by reflecting a driving pattern learnedby driver operation.

Additional advantages, objects, and features of the invention will beset forth in part in the description which follows and in part willbecome apparent to those having ordinary skill in the art uponexamination of the following or may be learned from practice of theinvention. The objectives and other advantages of the invention may berealized and attained by the structure particularly pointed out in thewritten description and claims hereof as well as the appended drawings.

To achieve these objects and other advantages and in accordance with thepurpose of the invention, as embodied and broadly described herein, anapparatus for controlling an autonomous vehicle may include a user inputunit configured to receive identification information of a driver withinthe vehicle during autonomous driving, an information collection unitconfigured to acquire a global route of the vehicle and surroundingenvironment information, and a controller configured to determine alearning section on the route based on the surrounding environmentinformation and output a driver driving pattern by performing repetitivelearning based on driver operation information in the learning section.The controller may be configured to execute autonomous driving of thevehicle based on the output driver driving pattern.

The surrounding environment information may include at least one of roadinformation, traffic information or obstacle information, collected bysensors mounted within the vehicle. The learning section may include atleast one of a delayed/congested situation, an acceleration/decelerationsituation, an unexpected situation, a straight section, a curvedsection, a downhill section or an electronic toll collection section.

The controller may be configured to switch from an autonomous drivingmode to a manual driving mode and transfer a vehicle control right tothe driver, when a current driving situation of the vehicle recognizedbased on the surrounding environment information corresponds to thelearning section. The driver operation information may include operationinformation regarding at least one of a steering wheel, an acceleratorpedal or a brake pedal, mounted within the vehicle.

The controller may be configured to learn the driver driving pattern byacquiring an error between an autonomous driving control value which isset in advance in the vehicle and at least one user control value whichis calculated based on the driver operation information using comparisontherebetween and updating the autonomous driving control value to causea gradient of the error to a learning rate to converge within a minimumcritical value. The controller may be configured to perform a validitytest of the at least one user control value by applying a compensationweight to the error.

The compensation weight may be set to a different value based on atleast one of a collision risk or a ride comfort index depending on thedriver operation information. Each of the autonomous driving controlvalue and the user control value may include at least one of a steeringangle, an acceleration, a deceleration, a relative speed, a turningradius, a lateral acceleration or a longitudinal acceleration of thevehicle.

The apparatus may further include a storage unit configured to generatea learning table according to the identification information of thedriver by constructing indexes for the learning section and the driverdriving pattern. The controller may be configured to extract at leastone boundary point on the route based on the driver driving pattern,generate a driving route by connecting a current position of the vehicleand the at least one boundary point through polynomial regressionanalysis, and execute the autonomous driving of the vehicle based on thegenerated driving route. The at least one boundary point may include apoint at which a change rate of a steering angle exceeds a criticalrange.

It is to be understood that both the foregoing general description andthe following detailed description of the present invention areexemplary and explanatory and are intended to provide furtherexplanation of the invention as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the invention and are incorporated in and constitute apart of this application, illustrate exemplary embodiment(s) of theinvention and together with the description serve to explain theprinciple of the invention. In the drawings:

FIG. 1 is a schematic block diagram of an apparatus for controlling anautonomous vehicle in accordance with one exemplary embodiment of thepresent invention;

FIG. 2 is a view illustrating the structure of a learning system appliedto the control apparatus in accordance with one exemplary embodiment ofthe present invention;

FIGS. 3A and 3B are views illustrating a method for generating a drivingroute through the control apparatus in accordance with one exemplaryembodiment of the present invention;

FIGS. 4 and 5 are views illustrating driving situations before and afterlearning of a driver's driving pattern by the control apparatus inaccordance with one exemplary embodiment of the present invention; and

FIG. 6 is a flowchart illustrating a method for controlling anautonomous vehicle in accordance with one exemplary embodiment of thepresent invention.

DETAILED DESCRIPTION

It is understood that the term “vehicle” or “vehicular” or other similarterm as used herein is inclusive of motor vehicles in general such aspassenger automobiles including sports utility vehicles (SUV), buses,trucks, various commercial vehicles, watercraft including a variety ofboats and ships, aircraft, and the like, and includes hybrid vehicles,electric vehicles, plug-in hybrid electric vehicles, hydrogen-poweredvehicles and other alternative fuel vehicles (e.g. fuels derived fromresources other than petroleum). As referred to herein, a hybrid vehicleis a vehicle that has two or more sources of power, for example bothgasoline-powered and electric-powered vehicles.

Although exemplary embodiment is described as using a plurality of unitsto perform the exemplary process, it is understood that the exemplaryprocesses may also be performed by one or plurality of modules.Additionally, it is understood that the term controller/control unitrefers to a hardware device that includes a memory and a processor. Thememory is configured to store the modules and the processor isspecifically configured to execute said modules to perform one or moreprocesses which are described further below.

Furthermore, control logic of the present disclosure may be embodied asnon-transitory computer readable media on a computer readable mediumcontaining executable program instructions executed by a processor,controller/control unit or the like. Examples of the computer readablemediums include, but are not limited to, ROM, RAM, compact disc(CD)-ROMs, magnetic tapes, floppy disks, flash drives, smart cards andoptical data storage devices. The computer readable recording medium canalso be distributed in network coupled computer systems so that thecomputer readable media is stored and executed in a distributed fashion,e.g., by a telematics server or a Controller Area Network (CAN).

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items.

Unless specifically stated or obvious from context, as used herein, theterm “about” is understood as within a range of normal tolerance in theart, for example within 2 standard deviations of the mean. “About” canbe understood as within 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%,0.1%, 0.05%, or 0.01% of the stated value. Unless otherwise clear fromthe context, all numerical values provided herein are modified by theterm “about.”

Reference will now be made in detail to the exemplary embodiments of thepresent invention, examples of which are illustrated in the accompanyingdrawings. However, the disclosure of the invention is not limited to theembodiments set forth herein and may be variously modified. In thedrawings, in order to clearly describe the present invention,description of elements which are not related to the present inventionis omitted, and the same or similar elements are denoted by the samereference numerals even though they are depicted in different drawings.

In addition, in the following description of the exemplary embodiments,the terms “first”, “second”, etc. may be used to describe variouselements, but do not limit these elements. It will be understood thatthese terms are used only to discriminate one element from otherelements, and do not limit the nature, sequence or order of thecorresponding element. It will be understood that terms which arespecially defined in consideration of the configurations and functionsof the exemplary embodiments are used only to describe the embodimentsand do not limit the scope of the exemplary embodiments.

In the following description of the exemplary embodiments, all termsincluding technical or scientific terms have the same meanings asgenerally understood by those skilled in the art to which the presentinvention pertains, unless defined otherwise. Further, in the followingdescription of the exemplary embodiments, generally used terms, such asterms defined in dictionaries, will be interpreted as having meaningscoinciding with contextual meanings in the related art, and are not tobe interpreted as having ideal or excessively formal meanings, unlessdefined clearly.

Hereinafter, an apparatus for controlling an autonomous vehicle inaccordance with each exemplary embodiment of the present invention willbe described with reference to the accompanying drawings.

FIG. 1 is a schematic block diagram of an apparatus for controlling anautonomous vehicle in accordance with one exemplary embodiment of thepresent invention. As exemplarily shown in FIG. 1, an apparatus 100 forcontrolling an autonomous vehicle may include a user input unit 110, aninformation collection unit 120, a controller 130, and a storage unit140. The controller 130 may be configured to operate the othercomponents of the apparatus 100.

The user input unit 110 may include a driver information input unit 111configured to receive identification information of a driver in thevehicle during autonomous driving, and an input signal selection unit112 configured to receive user input for an optimization request ofautonomous driving control based on a driving pattern of the driver. Thedriver information input unit 111 may be configured to receiveidentification information including biometric information and/orregistration information of at least one driver from an externalapparatus via a vehicle network 40 and store the identificationinformation in a database (DB; not shown) to generate driving patternsof various users. Particularly, the biometric information may includerecognition information regarding irises, fingerprints, face or voice ofthe user, and the registration information may include a driveridentification (ID) formed by a combination of Korean characters,English characters, numbers and special characters, or a contactinformation of the user, without being limited thereto.

The driver information input unit 111 may be configured to confirmwhether the identification information of the driver received from theexternal apparatus corresponds to identification information of thedriver stored in the database (not shown), and execute contact with thecontroller 130. The input signal selection unit 112 may be configured toconvert a designated input signal applied by the driver, for example, atleast one of an on signal, an off signal or a reset signal, into controlinstructions, and the driver may receive an optimization request ofautonomous driving control based on a driving pattern of the driver andswitch between driving modes of the vehicle by selecting at least oneinput part provided on the input signal selection unit 112.

Table 1 below exemplarily states control instructions processedaccording to the driver input signal applied to the input signalselection unit 112 and driving modes which are switched.

TABLE 1 Input signal Driving mode Control instructions 1. on ManualInitiation instructions to learn a driver's driving driving pattern(hereinafter, referred to as mode ‘leaning initiation instructions’) 2.off Autonomous Instructions to set a recently learned driver's drivingdriving pattern as default (hereinafter, referred mode to as ‘defaultsetting instructions’) 3. reset Autonomous Instructions to initialize adriving pattern of driving the autonomous vehicle set in advance beforemode coming onto the market (hereinafter, referred to as ‘initializationinstructions’)

Referring to Table 1, if an on signal is applied to the input signalselection unit 112 by the driver (e.g., an on signal is received as userinput), the driving mode of the vehicle may be switched from anautonomous driving mode to a manual driving mode. Particularly, theautonomous driving mode refers to a state in which the vehicle isautonomously driven without driver intervention, and the manual drivingmode refers to a state in which some of operations by the vehicle arerestricted in a specific driving situation.

Then, the input signal selection unit 112 may be configured to convertthe on signal into the learning initiation instructions and transmit thelearning initiation instructions to the controller 130. In response, thecontroller 130 may be configured to transfer a vehicle control right tothe driver in the specific driving situation, repeatedly learn driver'soperation information applied to a driving device 30 and thus output adriving pattern of the driver. The input signal selection unit 112 maybe configured to convert an off signal or a reset signal into thedefault setting instructions or the initialization instructions, and thecontroller 130 may be configured to perform autonomous driving of theautonomous vehicle by operating the driving device 30 using the recentlylearned driving pattern of the driver or the driving pattern of theautonomous vehicle set in advance before coming onto the market.

The user input unit 110 may be disposed within the vehicle, for example,in one region of an instrument panel, one region of a seat, one regionof a pillar, one region of a door, one region of a center console, oneregion of a head lining, one region of a windshield or one region of awindow. Further, a part of the user input unit 110 may be implemented asa button, which protrudes to the outside (or is dented), or a part orthe entirety of the user input unit 110 may be implemented as a touchpanel, and the user input unit 110 may be provided with at least oneinput part to detect various user inputs. For example, the input partmay include a key pad, a touchpad (resistive type/capacitive type), adome switch, a physical button or a jog shuttle.

The information collection unit 120 may include a global routecollection unit 121 and an environment information collection unit 122.The information collection unit 120 may be configured to acquire a route(e.g., a global route) and surrounding environment information fromvarious peripheral devices 10 and/or sensor devices 20 mounted withinthe vehicle. The information collection unit 120 may be configured toperform communication with the peripheral devices 10 and the sensordevices 20 via the vehicle network 40, and the vehicle network 40 mayinclude various in-vehicle communication standards, such as a controllerarea network (CAN), a CAN with flexible data rate (CAN-FD), FlexRay,media oriented systems transport (MOST), time triggered Ethernet (TTEthernet), etc.

The global route collection unit 121 may be configured to collect theroute based on information of a departure point and a destination inputto the peripheral device (hereinafter, referred to as a navigationsystem) 10. The navigation system 10 may be configured to store mapinformation, in which roads and lanes are distinguishable, as a database(DB), and the map information may include nodes and links. Particularly,the node refers to a point at which properties of a road are changed,and the link refers to a route in a lane which interconnects one nodeand another node (e.g., a first node and a second node). Such mapinformation may be updated automatically on a constant cycle viawireless communication, or updated manually by the user.

The environment information collection unit 122 may be configured tocollect various surrounding environment information regarding drivingsituations of the vehicle from the sensor devices 20 mounted within thevehicle. In particular, the sensor devices 20 may include, for example,a global positioning system (GPS) 21 configured to acquire positioninformation of the vehicle by receiving a navigation message from atleast one GPS satellite located above the earth, a camera 22 configuredto analyze surrounding image information of the vehicle using an opticalsystem, a radar device 23 configured to analyze a distance from anobject and relative speed to the object using electromagnetic waves, alidar device 24 configured to observe blind areas which are not visiblethrough radar, using light, and a steering angle sensor 25, a speedsensor 26 and an acceleration sensor 27 configured to measure steeringangle, absolute speed and acceleration information of the vehicle.

The environment information collection unit 122 may be configured tocollect the surrounding environment information including at least oneof road information, traffic information or obstacle information,through a combination of at least one of the above-described sensordevices 20. For example, the environment information collection unit 122may be configured to collect road information related to attributes ofroads, such as curvatures, grades, intersections, junctions, crosswalksand tollgates of the road, based on map information stored in thenavigation system 10 and surrounding image information analyzed usingthe camera 21.

Further, the environment information collection unit 122 may beconfigured to collect traffic information to recognize a delayed andcongested traffic situation, such as a density of objects distributedaround the vehicle, and/or obstacle information to recognize anunexpected situation, such as risk of obstacles installed on (or fallenonto) a road surface, based on at least one of the GPS 21, the radardevice 23, the lidar device 24, the steering angle sensor 25, the speedsensor 26 or the acceleration sensor 27. Although not shown in thedrawings, the environment information collection unit 122 may also beconfigured to collect surrounding environment information from othervehicles and objects in which infrastructure is constructed via vehicleto everything (V2X) communication.

The controller 130 may include a learning section determination unit131, a driving pattern learning unit 132, and a driving strategyestablishment unit 133. As the learning section determination unit 131receives the learning initiation instructions applied from the userinput unit 110, operation of the learning section determination unit 131may be activated.

The learning section determination unit 131 may be configured todetermine a learning section L on the route based on the varioussurrounding environment information collected by the informationcollection unit 120. For example, the learning section determinationunit 131 may be configured to detect a current driving situation of thevehicle by combining at least one of the road information, the trafficinformation or the obstacle information collected by the environmentinformation collection unit 120, and determine whether the recognizeddriving situation corresponds to the predetermined learning section L.

The learning section determination unit 131 may be configured to performlearning of only some driving situations that correspond to the learningsection L out of the route to reduce a load caused by repetitivelearning of the driving pattern learning unit 132, but the scope of theinvention is not limited thereto. Table 2 below exemplarily representsthe learning section L which is predetermined by the learning sectiondetermination unit 131.

TABLE 2 L1 L2 L3 L4 L5 L6 L7 Delayed/ Acceleration/ Unexpected StraightCurved Downhill Electronic congested deceleration situation sectionsection section toll situation situation collection section

Referring to Table 2, the learning section L may be defined as a set ofsituations which occur during driving of the vehicle, and include, forexample, a delayed/congested situation L1, an acceleration/decelerationsituation L2, an unexpected situation L3, a straight section L4, acurved section L5, a downhill section L6, and an electronic tollcollection section L7.

When the current driving situation of the vehicle corresponds to thelearning section L, the learning section determination unit 131 may beconfigured to switch from the autonomous driving mode to the manualdriving mode and transfer a vehicle control right to the driver (e.g.,with no controller intervention). As the driving pattern learning unit132 receives a vehicle control right transfer signal generated by thelearning section determination unit 131, operation of the drivingpattern learning unit 132 may be activated.

The driving pattern learning unit 132 may be configured to output adriving pattern of the driver by performing repetitive learning based ondriver's operation information reacting in the learning section L. Thedriver's operation information may refer to operation information whichthe driver gaining the vehicle control right applies to the drivingdevice 30 mounted within the vehicle. Particularly, the driving device30 may include a steering wheel 31 configured to adjust a direction ofthe vehicle, an accelerator pedal 32 configured to adjust a speed of thevehicle by adjusting an opening degree of a throttle of the vehicle, anda brake pedal 33 configured to decelerate the vehicle using frictionalforce.

The driving pattern learning unit 132 may be operated based on areinforcement learning algorithm which is a type of machine learning,and perform learning based on a policy to recognize surroundingenvironment information using mutual information exchange with a drivingsituation and to maximize an action value function acquired through thedriver's operation information (e.g., operation information of at leastone of the steering wheel 31, the accelerator pedal 32 or the brakepedal 33) applied to the vehicle.

The driving pattern learning unit 132 may be configured to learn adriving pattern of the driver by acquiring an error δ between a firstautonomous driving control value α which is set in advance in thevehicle based on set rules, and at least one user control value β_(i)which is calculated based on the driver's operation information throughcomparison therebetween, and updating the first autonomous drivingcontrol value α so that a gradient Δδ of the error δ to a learning rateη (Δδ=dδ/dη) converges on a minimum critical value, and output a secondautonomous driving control value α′ as a result of the above learning.

The first autonomous driving control value α refers a control value ofat least one parameter which is learned or set in advance in the vehiclebefore the vehicle comes onto the market to perform autonomous driving.Additionally, the first autonomous driving control value α may be set inadvance by a developer according to autonomous driving rules set basedon traffic regulations and safe driving requirements, and the same firstautonomous driving control value α may be applied to all drivers.

The user control value β_(i) refers to a control value of at least oneparameter which is sensed or calculated by the sensor device 20 inresponse dot various operation information which the driver gaining thevehicle control right applies to the driving device 30. The user controlvalue β_(i) may be varied according to the tendency, state, etc. of thedriver operating the driving device 30.

The second autonomous driving control value α′ refers to a control valueof at least one parameter on which the at least one user control valueβ_(i) converges (or into which the at least one user control value β_(i)is smoothed) by repetitive learning. Additionally, the second autonomousdriving control value α′ may be set as a default value to performautonomous driving of the vehicle after completion of learning.Particularly, the at least one parameter may include at least one ofsteering angle information, acceleration/deceleration information,heading/roll information calculated by various combinations thereof,relative speed information, lateral/longitudinal accelerationinformation or turning radius information.

Further, the error δ between the first autonomous driving control valueα and the at least one user control value β_(i) may be calculated usinga mean squared error (MSE), as stated in Equation 1 below. However, itis only exemplary, and the scope of the invention is not limitedthereto.

$\begin{matrix}{\delta = {\frac{1}{n}{\overset{n}{\underset{i = 1}{Q}}\left( {\alpha - \beta_{j}} \right)}^{2}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$wherein, n means a cumulative frequency of driver's operations appliedto the driving device 30 in the corresponding learning section, a meansa first autonomous driving control value, and β_(i) means a user controlvalue acquired by i^(th) operation (1≤i≤n).

The learning rate η may be defined as a rate of the cumulative frequencyof driver's operations to a critical learning frequency N, and expressedas

${\eta = {\frac{n}{N}\left( {0 < \eta \leq 1} \right)}}.$The critical learning frequency N may refer to a frequency ofconvergences of the error gradient Δδ within the minimum critical value,and the driving pattern learning unit 132 may be configured to output adriving pattern similar to an intention of the driver by performingrepetitive learning until a frequency of learnings reaches the criticallearning frequency N. In particular, the minimum critical value may be 0or a value close to 0, and consequently, the above policy to maximizethe action value function may be concluded as a policy to minimize thegradient Δδ of the error δ between the first autonomous driving controlvalue α and the at least one user control value β_(i).

Further, the driving pattern learning unit 132 may be configured toperform a validity test of the at least one user control value β_(i) byapplying a compensation weight w to the error δ. In particular, thecompensation weight w may be set to a different value according to atleast one of a collision risk w1 or a ride comfort index w2 based on thedriver's operation information. For example, the driving patternlearning unit 132 may be configured to set the compensation weight w to0 and thus treat the corresponding user control value as an ignorablevalue, if, as a result of driving according to the driver's operationinformation, the vehicle collides with a peripheral object or the ridecomfort index w2 is reduced to a critical value or less. Thecompensation weight w may be set so that the collision risk w1 isinversely proportional to a time to collision (TTC) and the ride comfortindex w2 is inversely proportional to the size of a jerk (a change rateof an acceleration).

Accordingly, the driving pattern learning unit 132 may be configured toperform the validity test of the at least one user control value β_(i),and may thus promote vehicle safety and reduce sense of difference feltor experienced by the driver and passengers during operation of thevehicle. The driving strategy establishment unit 133 may be configuredto transmit the driving pattern of the driver or the second autonomousdriving control value α′ output through the driving pattern learningunit 132 to the driving device 30, thus executing autonomous driving ofthe vehicle.

Further, the driving strategy establishment unit 133 may be configuredto extract at least one boundary point on the route based on the outputdriving pattern of the driver, generate a driving route by connectingthe current position of the vehicle and the at least one boundary pointthrough polynomial regression analysis, and execute autonomous drivingof the vehicle based on the generated driving route. In particular, theat least one boundary point may include a point at which a change rateof the steering angle exceeds a critical range, and a detaileddescription thereof will be given later with reference to FIG. 3.

The storage unit 140 may be configured to generate and store a lookuptable 141 and at least one learning table 142 that corresponds to driveridentification information. The lookup table 141 may include the firstautonomous driving control value α which is set in advance by thedeveloper according to autonomous driving rules set based on trafficregulations and safe driving requirements, and be applied to alldrivers.

Table 3 below exemplarily represents autonomous driving rules togenerate the lookup table 141.

TABLE 3 Control parameter Autonomous driving rules Steering angleprohibition of violation of a centerline information set of a steeringdirection to recognized available space determination of a steeringangle based on aTTC, a road curvature, and a critical value of a changerate of the steering angle per unit time Acceleration/ maintenance of asafe distance from a front vehicle deceleration emergency braking whenan obstacle information is suddenly recognized determination ofacceleration/deceleration based on the TTC, a road speed limit, a roadgradient, and a critical value of a change rate ofacceleration/deceleration per unit time

The at least one learning table 142 may be generated by generatingindexes for the learning section L and the driving pattern of the driveror the second autonomous driving control value α′. In particular, eachof the at least one the learning table 142 may be generated tocorrespond to each of at least one driver identification information(e.g., user1, user2, user3, etc.), and thereby, customized autonomousdriving control according to driving tendencies of various users may bepossible and user's individual ride comfort requirements may besatisfied. The storage unit 140 may be implemented as at least onestorage medium of a flash memory, a hard disk, a secure digital (SD)card, a random access memory (RAM), a read only memory (ROM), and a webstorage.

FIG. 2 is a view illustrating the structure of a learning system appliedto the control apparatus in accordance with one exemplary embodiment ofthe present invention. Referring to FIG. 2, a learning system accordingto a driver's driving pattern represents an interface between an agentand an environment. Particularly, the agent refers to the controlapparatus 100, and the environment refers to a driving situation.

The control apparatus 100 may be operated based on a reinforcementlearning algorithm, and use Q-learning which is a type of reinforcementlearning, to construct an optimum control strategy. Q-learning isexcellent in terms of applicability and portability and has a highcomputation speed, thus being effective in learning of driver's drivingpatterns in various driving environments. The control apparatus 100 maybe configured to recognize a current state through mutual informationexchange with a driving situation, and select an action to maximize areward r, from actions which are selectable.

The state s refers to surrounding environment information including atleast one of the road information, the traffic information or theobstacle information collected by the environment information collectionunit 120, and the action a refers to driver's operation informationincluding at least one of steering wheel operation information,accelerator pedal operation information or brake pedal operationinformation.

Additionally, the reward r refers to a result of the action a which isacquired by transitioning the current state s to a next state s′, andmay be expressed as a scalar value indicating whether the driver'saction a is correct. For example, the control apparatus 100 may beconfigured to perform learning by providing a reward when a result ofdriving according to the driver's operation information is correct andproviding a penalty when the result of driving is incorrect, and maythus establish an optimum policy which accords with the driver's drivingpattern and intention. Particularly, the action value function(Q-function) of the reward r acquired during repetition of statetransition may be expressed as Equation 2 below.Q ^(π)(s,a)=E _(π)[r _(t+1) +γr _(t+2)+γ² r _(t+3)+ . . . ]=E_(π)[r(s,a)+γQ ^(π)(s′,a′)   Equation 2wherein, s′ is a next state which appears when the action a is taken inthe current state s, a′ is all actions which may be taken in the nextstate s′, and γ means a discount factor which determines how much afuture driving condition will influence the current learning and is setto be within a numeral range from 0 to 1 (0≤γ≤1). Q^(π)(s,a) is theaction value function, and means an expected value of a cumulativereward acquired when a series of policies π is followed.

The control apparatus 100 may be configured to perform repetitivelearning until the action value function Q^(π)(s,a) converges on amaximum value based on an optimum policy π*, and the optimum policy π*may be expressed as Equation 3 below.π*(s)=arg max Qπ(s,a)  Equation 3wherein, the policy to maximize the action value function is concludedas the policy to minimize the gradient Δδ of the error δ between thefirst autonomous driving control value α and the at least one usercontrol value β_(i), and the control apparatus 100 may be configured toperform repetitive learning until the gradient Δδ of the error δ to thelearning rate η converges within the minimum critical value.

FIGS. 3A-3B are views illustrating a method for generating a drivingroute through the control apparatus in accordance with one exemplaryembodiment of the present invention. Referring to FIG. 3A, the drivingstrategy establishment unit 133 may be configured to divide the routeacquired by the information collection unit 120 into at least one routesection P, and calculate position coordinate data of a start point S andan end point F of the route section P by converting GPS information ofthe vehicle into Transverse Mercator (TM) coordinates which are XYabsolute coordinates. In particular, the start point S of the routesection P may correspond to the current position of the vehicle.

Further, the driving strategy establishment unit 133 may be configuredto extract at least one boundary point N₁, N₂, N₃ and N₄ between thestart point S and the end point F of the route section P based on thedriving pattern of the driver output from the driving pattern learningunit 132. Particularly, the at least one boundary point N₁, N₂, N₃ andN₄ may include a point at which a change rate of the steering angleexceeds the critical range.

Referring to FIG. 3B, the driving strategy establishment unit 133 beconfigured to calculate a route equation using polynomial regressionanalysis, and generate a driving route DR by connecting the start pointS and the end point F of the route section P and the at least oneboundary point N₁, N₂, N₃ and N₄. In particular, the driving route DRmay be expressed as an m^(th)-order polynomial route equation, i.e.,Equation 4 below.y=a ₀ +a ₁ x+a ₂ x ² + . . . +a _(m) x ^(m)  Equation 4wherein, x and γ mean position coordinate data, and a₀, a₁, a₂, . . . ,a_(m) mean known values to be found, i.e., coefficients of the routeequation.

The driving strategy establishment unit 133 may be configured tocalculate position coordinate data of each of the at least one boundarypoint N₁, N₂, N₃ and N₄ based on the steering angle information, theacceleration/deceleration information, the lateral/longitudinalacceleration information, the turning radius information, etc. of thesecond autonomous driving control value α′ output from the drivingpattern learning unit 132 through repetitive learning, and calculate thecoefficients a₀, a₁, a₂, . . . , a_(m) of the route equation.

For example, the driving strategy establishment unit 133 may beconfigured to calculate moving distances of the vehicle in thelongitudinal and lateral directions in moving sections (e.g., N₁-N₂,N₂-N₃ and N₃-N₄) between the boundary points using an angle ofdeflection θ1, a turning angle θ2, a radius of rotation R andlateral/longitudinal accelerations a_(x) and a_(y) based on the drivingpattern of the driver, and calculate the position coordinate data ofeach of the at least one boundary point N₁, N₂, N₃ and N₄ based on theposition coordinate data of the start point S and the end point F of theroute section P.

Further, the driving strategy establishment unit 133 may be configuredto calculate the coefficients a₀, a₁, a₂, . . . , a_(m) of them^(th)-order polynomial route equation by substituting the positioncoordinate data of the start point S, the end point F and each of the atleast one boundary point N₁, N₂, N₃ and N₄ into the route equation. Inparticular, the number of at least one boundary point N₁, N₂, N₃ and N₄extracted by the driving strategy establishment unit 133 may be m−1 atthe least.

The driving strategy establishment unit 133 may be configured togenerate the driving route DR based on the calculated m^(th)-orderpolynomial route equation, and perform autonomous driving of the vehiclebased on the generated driving route DR. In particular, the storage unit140 may be configured to store route equations of driving routes DR ofrespective route sections calculated by the driving strategyestablishment unit 133.

FIGS. 4 and 5 are views illustrating driving situations before and afterlearning of a driving pattern of the driver by the control apparatus inaccordance with one exemplary embodiment of the present invention.Referring to FIG. 4, a current driving situation corresponds to thedelay/congested situation L1 in which other vehicles, i.e., a vehicle Bdriven in a driving lane of a host vehicle A and a vehicle in aperipheral lane adjacent to the driving lane of the host vehicle A, aredistributed densely (referring to Table 2).

Particularly, it may be assumed that the current speed of the hostvehicle A (e.g., host vehicle or subject vehicle) is 50 kph, the currentspeed of the vehicle B located in front of the host vehicle A in thedriving lane is 50 kph, and the current speed of the vehicle Capproaching the host vehicle A from behind in the peripheral lane is 70kph. Before learning by the control apparatus 100 of the presentinvention (in (a) of FIG. 4), the host vehicle A may be driven in thedriving lane at a speed of 50 kph while maintaining a safe distance fromthe other vehicle B (e.g., second vehicle) by calculating the time tocollision (TTC) with the other vehicle C (e.g., third vehicle) accordingto the autonomous driving rules set in advance (referring to Table 3).Such a driving pattern contributes to improvement in vehicle safety, buthas limits in reflection of driving tendencies of various drivers sincesome drivers may desire to tune the driving pattern to suit personaltastes.

On the other hand, after learning by the control apparatus 100 of thepresent invention (in (b) of FIG. 4), the host vehicle A may accelerateat a speed of 100 kph within a range causing no collision with the othervehicle C approaching the host vehicle A in the peripheral laneaccording to a driving pattern in which a driver's dynamic tendency isreflected, and perform lane change to a right lane. Accordingly, thecontrol apparatus 100 in accordance with one exemplary embodiment mayflexibly cope with various situations according to the driver's drivingtendency, escaping from a uniform driving pattern, thus enablingdriver-customized driving. Thereby, sense of difference between realdriving by the driver and autonomous driving may be reduced, and ridecomfort of the autonomous vehicle may be improved.

Referring to FIG. 5, a current driving situation corresponds to theunexpected situation L3 in which an object falls onto a driving lanefrom another vehicle B located in front of a host vehicle A (referringto Table 2). Particularly, a road is a two-lane road which has one laneeach way, and it is assumed that no vehicle is present in another lanein the opposite direction to the driving lane.

Before learning by the control apparatus 100 of the present invention(in (a) of FIG. 5), the host vehicle A may be stopped until the fallenobject C is removed under the condition that the host vehicle A does notviolate the centerline according to the autonomous driving rules set inadvance (referring to Table 3). However, such a passive driving patterninterrupts traffic flow on the two-lane road and causes sense ofdifference between real driving by a driver and autonomous driving.

On the other hand, after learning by the control apparatus 100 of thepresent invention (in (b) of FIG. 5), the host vehicle A may confirmwhether a vehicle is present in the opposite lane, and overtake theother vehicle B by violating the centerline to avoid the fallen objectC. Accordingly, the control apparatus 100 in accordance with oneexemplary embodiment may flexibly cope with a sudden unexpectedsituation, thus reducing sense of difference between real driving by adriver and autonomous driving without interrupting the traffic flow.

FIG. 6 is a flowchart illustrating a method for controlling anautonomous vehicle in accordance with one exemplary embodiment of thepresent invention. Referring to FIG. 6, a method 600 for controlling anautonomous vehicle may include receiving designated information from auser (Operation S610), learning a driver's driving pattern (OperationS620), and executing autonomous driving (Operation S630).

First, the user input unit 110 may be configured to receiveidentification information of a driver in the vehicle during vehicleoperation to generate driving patterns of various users (OperationS611). Thereafter, the user input unit 10 may be configured to determinewhether user input for an optimization request of autonomous drivingcontrol based on a driving pattern of the driver is received (OperationS612). When the user input for the optimization request of autonomousdriving control based on the driving pattern of the driver is notreceived from the user input unit 10 (No in Operation S612), thecontroller 130 may be configured to execute autonomous driving of thevehicle using a recently learned driving pattern of the driver and/or adriving pattern of the autonomous vehicle set in advance before comingonto the market (Operation S632).

On the other hand, when the user input for the optimization request ofautonomous driving control based on the driving pattern of the driver isreceived from the user input unit 10 (Yes in Operation S612), theinformation collection unit 120 may be configured to acquire a route andsurrounding environment information from the peripheral devices 10and/or the sensor devices 20 mounted within the vehicle (OperationS621). Particularly, the surrounding environment information may includeat least one of road information, traffic information or obstacleinformation.

Thereafter, the controller 130 may be configured to determine whether alearning section is present on the route based on the varioussurrounding environment information collected by the informationcollection unit 120 (Operation S622). For example, the learning sectionmay include a delayed/congested situation, an acceleration/decelerationsituation, an unexpected situation, a straight section, a curvedsection, a downhill section, and an electronic toll collection section.

When the current driving situation of the vehicle does not correspond tothe learning section (No in Operation S622), the control method S600 mayreturn to Operation S621. On the other hand, when the current drivingsituation of the vehicle corresponds to the learning section (Yes inOperation S622), the controller 130 may be configured to switch from theautonomous driving mode to the manual driving mode and transfer avehicle control right to the driver (Operation S623). Thereafter, thecontroller 130 may be configured to acquire driver's operationinformation in which the driver gaining the vehicle control rightapplies to the driving device 30 of the vehicle (Operation S624). Inparticular, the driver's operation information may include operationinformation of at least one of the steering wheel, the accelerator pedalor the brake pedal.

Thereafter, the controller 130 may be configured to acquire an error δbetween a first autonomous driving control value α which is set inadvance in the vehicle based on set rules, and at least one user controlvalue β_(i) which is calculated based on the driver's operationinformation through comparison therebetween, and learn a driving patternof the driver by updating the first autonomous driving control value αso that a gradient Δδ of the error δ to a learning rate η (Δδ=dδ/dη)converges on a minimum critical value (Operation S625). In particular, areinforcement learning algorithm which is a type of machine learning maybe used as one example of a learning method.

Further, the controller 130 may be configured to perform a validity testof the at least one user control value β_(i) by applying a compensationweight w to the error δ (Operation S625). Particularly, the compensationweight w may be set to a different value according to at least one of acollision risk w1 or a ride comfort index w2 based on the driver'soperation information. Thereafter, the controller 130 may be configuredto determine whether a cumulative frequency n of driver's operationsreaches a critical learning frequency N (Operation S626). In particular,the cumulative frequency n of driver's operations refers to a cumulativevalue of the frequencies of driver's operations applied to the drivingdevice 30 in the corresponding learning section, and the criticallearning frequency N refers to a frequency of convergences of the errorgradient Δδ within a minimum critical value.

The controller 130 may be configured to repeatedly perform OperationsS621 to S625 until the cumulative frequency n of driver's operationsreaches the critical learning frequency N (Yes in Operation S626). As aresult of determination of Operation S626, when the cumulative frequencyn of driver's operations reaches the critical learning frequency N (Noin Operation S626), the controller 130 may be configured to output anautonomous driving control value α′ updated based on the learned drivingpattern of the driver, and the storage unit 140 may be configured togenerate at least one learning table corresponding to driveridentification information (Operation S627). Particularly, each of theat least one learning table 142 may be generated to correspond to eachof at least one driver identification information (user1, user2, user3,etc.) by constructing indexes for the learning section L and the drivingpattern of the driver or the updated autonomous driving control valueα′.

Thereafter, the controller 130 may be configured to extract at least oneboundary point on the route based on the learned driving pattern of thedriver, and generate a driving route by connecting the current positionof the vehicle and the at least one boundary point through polynomialregression analysis (Operation S631). Such Operation was described abovein reference to FIGS. 3A and 3B and a detailed description thereof willbe omitted. Thereafter, the controller 130 may be configured to executeautonomous driving of the vehicle based on the generated driving routeand the learned driving pattern of the driver (Operation S632).

The above-described control method in accordance with one exemplaryembodiment may be recorded as a program which may be executed incomputers and be stored in a non-transitory computer readable recordingmedium and, for example, non-transitory computer readable recordingmedia may include a ROM, a RAM, a CD-ROM, a magnetic tape, a floppydisk, an optical data storage device, etc.

The non-transitory computer readable recording media may be distributedto computer systems connected by a network and, herein, computerreadable code may be stored and executed in a distributed manner.Further, functional programs, code and code segments to implement theabove-described method may be easily deduced by programmers in the artto which the embodiments pertain.

As apparent from the above description, an apparatus and method forcontrolling an autonomous vehicle may flexibly cope with a drivingsituation according to a driver's driving tendency, escaping from auniform driving pattern, thus executing user-friendly autonomous drivingcontrol. Therefore, sense of difference between real driving by a driverand autonomous driving may be reduced. Further, learning tables ofvarious users may be constructed, and thus, a driver-customizedautonomous driving strategy may be used, and individual ride comfortrequirements of the users may be satisfied.

While the invention has been explained in relation to the embodimentsthereof, it will be understood that various modifications thereof willbecome apparent to those skilled in the art upon reading thespecification. Technical contents of the above-described embodiments maybe combined into various types unless they are mutually incompatible,and thereby, new embodiments may be implemented.

It will be apparent to those skilled in the art that variousmodifications and variations can be made in the present inventionwithout departing from the spirit or scope of the invention. Thus, it isintended that the present invention cover the modifications andvariations of this invention provided they come within the scope of theappended claims and their equivalents.

What is claimed is:
 1. An apparatus for controlling an autonomousvehicle, comprising: a user input unit including at least one processorand configured to receive identification information of a driver in thevehicle during autonomous driving; an information collection unitincluding at least one processor and configured to acquire a globalroute of the vehicle and surrounding environment information; and acontroller configured to determine a learning section on the globalroute based on the surrounding environment information and output adriving pattern of the driver by performing repetitive learning based onoperation information of the driver in the learning section, wherein thecontroller is configured to execute autonomous driving of the vehiclebased on the output driving pattern of the driver; wherein thecontroller is configured to learn the driving pattern of the driver byacquiring an error between an autonomous driving control value which isset in advance in the vehicle and at least one user control value whichis calculated based on the operation information of the driver throughcomparison therebetween and updating the autonomous driving controlvalue so that a gradient of the error to a learning rate coverageswithin a minimum critical value, wherein the controller configured toperform a validity test of the at least one user control value byapplying a compensation weight w to the error, wherein the compensationweight w is set to a different value according to at least one of acollision risk w1 and a ride comfort index w2 based on the operationinformation of the driver, and wherein the compensation weight w is setso that the collision risk w1 is inversely proportional to a timecollision (TTC) and the ride comfort index w2 is inversely proportionalto the size of a jerk.
 2. The apparatus according to claim 1, whereinthe surrounding environment information includes at least one selectedfrom the group consisting of: road information, traffic information, andobstacle information, collected by sensors mounted within the vehicle.3. The apparatus according to claim 1, wherein the learning sectionincludes at least one selected from the group consisting of: adelayed/congested situation, an acceleration/deceleration situation, anunexpected situation, a straight section, a curved section, a downhillsection, and an electronic toll collection section.
 4. The apparatusaccording to claim 1, wherein the controller is configured to switchfrom an autonomous driving mode to a manual driving mode and transfer avehicle control right to the driver, when a current driving situation ofthe vehicle recognized based on the surrounding environment informationcorresponds to the learning section.
 5. The apparatus according to claim1, wherein the operation information of the driver includes operationinformation of at least one selected from the group consisting of: asteering wheel, an accelerator pedal, and a brake pedal, mounted withinthe vehicle.
 6. The apparatus according to claim 1, wherein each of theautonomous driving control value and the user control value includes atleast one selected from the group consisting of: a steering angle, anacceleration, a deceleration, a relative speed, a turning radius, alateral acceleration, and a longitudinal acceleration of the vehicle. 7.The apparatus according to claim 1, further comprising a storage unitconfigured to generate a learning table according to the identificationinformation of the driver by generating indexes for the learning sectionand the driving pattern of the driver.
 8. The apparatus according toclaim 1, wherein the controller is configured to: extract at least oneboundary point on the global route based on the driving pattern of thedriver; generate a driving route by connecting a current position of thevehicle and the at least one boundary point through polynomialregression analysis; and execute the autonomous driving of the vehiclebased on the generated driving route.
 9. The apparatus according toclaim 8, wherein the at least one boundary point includes a point atwhich a change rate of a steering angle exceeds a critical range.
 10. Amethod for controlling an autonomous vehicle, comprising: receiving, bya controller, identification information of a driver within the vehicleduring autonomous driving; acquiring, by the controller, a global routeof the vehicle and surrounding environment information from sensorsmounted within the vehicle; determining, by the controller, a learningsection on the global route based on the surrounding environmentinformation; outputting, by the controller, a driving pattern of thedriver by performing repetitive learning based on operation informationof the driver in the learning section; and executing, by the controller,autonomous driving of the vehicle based on the output driving pattern ofthe driver; wherein the outputting of the driving pattern of the drivercomprises: learning by the controller, the driving pattern of the driverby acquiring an error between an autonomous driving control value whichis set in advance in the vehicle and at least one user control valuewhich is calculated based on the operation information of the driverthrough comparison therebetween and updating the autonomous drivingcontrol value so that a gradient of the error to a learning ratecoverages within a minimum critical value, updating, by the controller,the autonomous driving control value so that a gradient of the error toa learning rate coverages within a minimum critical value, wherein thelearning of the driving pattern of the driver includes performing, bythe controller, a validity test of the at least one user control valueby applying a compensation weight to the error, wherein the compensationweight w is set to a different value according to at least one of acollision risk w1 and a ride comfort index w2 based on the operationinformation of the driver, wherein the compensation weight w is set sothat the collision risk w1 is inversely proportional to a time collision(TTC) and the ride comfort index w2 is inversely proportional to thesize of a jerk.
 11. The method according to claim 10, wherein thedetermining of the learning section includes: switching, by thecontroller, from an autonomous driving mode to a manual driving mode andtransferring a vehicle control right to the driver, when a currentdriving situation of the vehicle recognized based on the surroundingenvironment information corresponds to the learning section.
 12. Themethod according to claim 10, further comprising: generating, by thecontroller, a learning table according to the identification informationof the driver by generating indexes for the learning section and thedriver's driving pattern.
 13. The method according to claim 10, furthercomprising: extracting, by the controller, at least one boundary pointon the global route based on the driving pattern of the driver; andgenerating, by the controller, a driving route by connecting a currentposition of the vehicle and the at least one boundary point throughpolynomial regression analysis, wherein, in the executing of theautonomous driving of the vehicle, the autonomous driving of the vehicleis performed based on the generated driving route.
 14. The methodaccording to claim 13, wherein the at least one boundary point includesa point at which a change rate of a steering angle exceeds a criticalrange.
 15. A non-transitory computer readable recording medium havingrecorded thereon an application program, which when executed by aprocessor, causes the processor to perform the method according to claim10.